I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

INTRODUCTION: Brief History. Multifingered Hands and Dextrous Manipulation. Outline of the Book. Bibliography. RIGID BODY MOTION: Rigid Body Transformations. Rotational Motion in R3. Rigid Motion in R3. Velocity of a Rigid Body. Wrenches and Reciprocal Screws. MANIPULATOR KINEMATICS: Introduction. Forward Kinematics. Inverse Kinematics. The Manipulator Jacobian. Redundant and Parallel Manipulators. ROBOT DYNAMICS AND CONTROL: Introduction. Lagrange's Equations. Dynamics of Open-Chain Manipulators. Lyapunov Stability Theory. Position Control and Trajectory Tracking. Control of Constrained Manipulators. MULTIFINGERED HAND KINEMATICS: Introduction to Grasping. Grasp Statics. Force-Closure. Grasp Planning. Grasp Constraints. Rolling Contact Kinematics. HAND DYNAMICS AND CONTROL: Lagrange's Equations with Constraints. Robot Hand Dynamics. Redundant and Nonmanipulable Robot Systems. Kinematics and Statics of Tendon Actuation. Control of Robot Hands. NONHOLONOMIC BEHAVIOR IN ROBOTIC SYSTEMS: Introduction. Controllability and Frobenius' Theorem. Examples of Nonholonomic Systems. Structure of Nonholonomic Systems. NONHOLONOMIC MOTION PLANNING: Introduction. Steering Model Control Systems Using Sinusoids. General Methods for Steering. Dynamic Finger Repositioning. FUTURE PROSPECTS: Robots in Hazardous Environments. Medical Applications for Multifingered Hands. Robots on a Small Scale: Microrobotics. APPENDICES: Lie Groups and Robot Kinematics. A Mathematica Package for Screw Calculus. Bibliography. Index Each chapter also includes a Summary, Bibliography, and Exercises

http://www.cds.caltech.edu/%7Emurray/books/MLS/pdf/mls94-complete.pdf

A Mathematical Introduction to Robotic Manipulation

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

In this paper, we present a theoretical framework for design and analysis of distributed flocking algorithms. Two cases of flocking in free-space and presence of multiple obstacles are considered. We present three flocking algorithms: two for free-flocking and one for constrained flocking. A comprehensive analysis of the first two algorithms is provided. We demonstrate the first algorithm embodies all three rules of Reynolds. This is a formal approach to extraction of interaction rules that lead to the emergence of collective behavior. We show that the first algorithm generically leads to regular fragmentation, whereas the second and third algorithms both lead to flocking. A systematic method is provided for construction of cost functions (or collective potentials) for flocking. These collective potentials penalize deviation from a class of lattice-shape objects called /spl alpha/-lattices. We use a multi-species framework for construction of collective potentials that consist of flock-members, or /spl alpha/-agents, and virtual agents associated with /spl alpha/-agents called /spl beta/- and /spl gamma/-agents. We show that migration of flocks can be performed using a peer-to-peer network of agents, i.e., "flocks need no leaders." A "universal" definition of flocking for particle systems with similarities to Lyapunov stability is given. Several simulation results are provided that demonstrate performing 2-D and 3-D flocking, split/rejoin maneuver, and squeezing maneuver for hundreds of agents using the proposed algorithms.

/pdf/flocking-for-multi-agent-dynamic-systems-algorithms-and-5efqu9aidp.pdf

Flocking for multi-agent dynamic systems: algorithms and theory

Conventionally, engineers have employed rigid materials to fabricate precise, predictable robotic systems, which are easily modelled as rigid members connected at discrete joints. Natural systems, however, often match or exceed the performance of robotic systems with deformable bodies. Cephalopods, for example, achieve amazing feats of manipulation and locomotion without a skeleton; even vertebrates such as humans achieve dynamic gaits by storing elastic energy in their compliant bones and soft tissues. Inspired by nature, engineers have begun to explore the design and control of soft-bodied robots composed of compliant materials. This Review discusses recent developments in the emerging field of soft robotics.

/pdf/design-fabrication-and-control-of-soft-robots-51z07seyl1.pdf

Design, fabrication and control of soft robots

Programming is a key aspect in the development and practical use of complex robotic systems. The work presented in this paper describes developments conducted within Inria’s Orccad and MaestRo programming framework for a robotic application, in which the Stanford mobile platform provides assistance to humans for performing physical tasks. This type of applications underlines the need for a well-designed programming architecture, without which such development becomes easily inextricable. The objective of this work is to study the portability and generality of the Orccad and MaestRo framework by an implementation on an unfamiliar field of application (robotic assistance) and on a non-native platform.

A Portable Programming Framework

This article investigates the problem of manipulator design for increased dynamic performance. Optimization techniques are used to determine the design parameters that improve manipulator performance. The dynamic performance of a manipulator is characterized by the inertial and acceleration properties of the end-effector. Our studies of inertial and acceleration properties have provided separate descriptions of the characteristics associated with linear and angular motions. This allows a more physically meaningful interpretation of these properties. The article presents these models, discusses the design optimization criteria, and formulates the optimization problem. The approach is illustrated in the design parameter selection of a parallel mechanism.

Optimization of the Inertial and Acceleration Characteristics of Non-Redundant Manipulators

This paper introduces a coordinate transform method for global/inertial sensor fusion minimizing modification of an existing control program of a mobile robot Most of GPS/INS sensor fusion algorithms use Kalman filters and modify the INS states by feedback loops. Because the structure of the proposed method has a feedforward filter, the proposed method has an advantage in case the user does not want to change an existing control program of mobile robot. The feedback type Kalman filter is designed so that the error between global position from GPS and odometry from INS converges to zero. Therefore the coordinate matching between the odometry and measured global position is not necessary in those approaches. However, in the feedforward structure described in this paper, the errors gradually increase over time. A coordinate transform method has been developed for dealing with the error. This method provides an easy way to make an add-on function without any changes in the existing functions of the control program of a mobile robot

Feedforward global/inertial sensor fusion algorithm for accurate global positioning of a mobile robot

This book constitutes the refereed proceedings of the 4th International Conference on Social Robotics, ICSR 2012, held in Chengdu, China, in October 2012. The 66 revised full papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on affective and cognitive sciences for socially interactive robots, situated interaction and embodiment, robots to assist the elderly and persons with disabilities, social acceptance of robots and their impact to the society, artificial empathy, HRI through non-verbal communication and control, social telepresence robots, embodiments and networks, interaction and collaboration among robots, humans and environment, human augmentation, rehabilitation, and medical robots I and II.

Oussama Khatib

Papers

A Portable Programming Framework

Optimization of the Inertial and Acceleration Characteristics of Non-Redundant Manipulators

Feedforward global/inertial sensor fusion algorithm for accurate global positioning of a mobile robot

Social Robotics: 4th International Conference, ICSR 2012, Chengdu, China, October 29-31, 2012, Proceedings

Practical connection between potential fields and neural networks